Reviewer training to assess knowledge translation in funding applications is long overdue
Bibliographic record
Abstract
BACKGROUND: Health research funding agencies are placing a growing focus on knowledge translation (KT) plans, also known as dissemination and implementation (D&I) plans, in grant applications to decrease the gap between what we know from research and what we do in practice, policy, and further research. Historically, review panels have focused on the scientific excellence of applications to determine which should be funded; however, relevance to societal health priorities, the facilitation of evidence-informed practice and policy, or realizing commercialization opportunities all require a different lens. DISCUSSION: While experts in their respective fields, grant reviewers may lack the competencies to rigorously assess the KT components of applications. Funders of health research-including health charities, non-profit agencies, governments, and foundations-have an obligation to ensure that these components of funding applications are as rigorously evaluated as the scientific components. In this paper, we discuss the need for a more rigorous evaluation of knowledge translation potential by review panels and propose how this may be addressed. CONCLUSION: We propose that reviewer training supported in various ways including guidelines and KT expertise on review panels and modalities such as online and face-to-face training will result in the rigorous assessment of all components of funding applications, thus increasing the relevance and use of funded research evidence. An unintended but highly welcome consequence of such training could be higher quality D&I or KT plans in subsequent funding applications from trained reviewers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.829 | 0.935 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".